Antwerp, Belgium - November 30th, 2025

If you're in FMCG, you'll know; the retail power balance is shifting. As generative AI (GenAI) transforms the traditional shopping experience from manual product selection to algorithmic curation, retailers evolve from distribution channel to brand-makers (or -breakers) with unprecedented control over consumer choice.

In this article our Director of Consumer Products, Tim Van den Bergh, shares his strategic insights on how generative AI is impacting the retail landscape and answers an important question every brand manager should be asking: How do you stay visible for shoppers when an AI assistant builds the basket?

For years, consumer brands have been spending millions in an attempt to win the shelf. They have been chasing better data, designing sharper packaging, making stronger claims, and sending out marketing campaigns built to resonate perfectly with their ideal customer profile.

But getting that competitive edge took time.

"While A-brands spend years and years paying millions to market research companies like Nielsen and Circana for 'sell-out insights', retailers have been able to build their own data. And that data is power", Tim notes.

Retailers today aren’t just selling products anymore. They’re running some of the most advanced data ecosystems in the world. From Walmart and Target in America to Carrefour in Europe and Alibaba in Asia; they all operate data intelligence systems that capture every click, cart addition, and abandoned purchase. Consequently, they understand personas better than many brand marketers out there.

And with AI-driven traffic to retail websites growing 4,700% year-over-year according to Adobe Analytics, chances are the amount of valuable customer insights will only grow in the next few years to come.

"If retailers look closely at all of the data at their disposal, they know exactly who buys what, when, and why. They see which price points convert, which sustainability claims resonate, and which flavors trend. They don’t need a six-month brand campaign to identify winning formulas as they track behaviors across loyalty cards, online carts, and even in-store patterns. That kind of data gives retailers something brands have always fought for: real-time consumer intimacy."

And it doesn't take much for forward-thinking retailers to use this huge pile of data to their advantage. According to Talk Business & Politics data, Walmart's Bettergoods and Target's Dealworthy brands increased sales volume by 200% in their first year. In Europe, retailers like Albert Heijn's Terra line and Jumbo's private portfolio compete directly with premium nationals.

"These aren’t generic white-label products anymore", Tim notes. "They’re sharply positioned, highly relevant, emotionally resonant, and increasingly premium. So this is where it becomes challenging for popular consumer brands."

At Made, we believe that the impact of GenAI on the dynamics of traditional brand loyalty is significant.

"People have been buying goods online for quite some time now, but they still need to click every item manually. It's like they’re filling a physical basket in-store. With the arrival of GenAI, however, we're seeing retailers being perfectly positioned to completely reshape the online shopping experience", Tim notes.

When Walmart announced its OpenAI partnership for 'Instant Checkout' functionality, they painted a future that should concern every brand manager: customers can simply chat with AI assistants about their needs, and the AI assistant will drop matching products in their carts.

No manual browsing. No brand comparison. No consideration.

"This signals a deep shift in brand loyalty", Tim explains the implications. "When a consumer searches for a small bag of crisps that’s healthy, spicy in flavour and sustainably sourced, GenAI doesn't show them a shelf of options to choose from; it will directly select products based on the specifics of the query and the retailer's algorithm. That's where GenAI fundamentally changes the game, because that AI could easily and perhaps inevitably favor a retailer's private label SKUs or whichever supplier offers the best margin structure. It’s not malicious, though. It’s optimization and if your brand isn't optimized for that algorithm, you're invisible and your online sales will drop."

If brand discovery and product recommendation are mediated by GenAI systems owned by retailers, the power dynamics flip completely.

"While this sounds dystopian for some, the data shows otherwise", Tim notes. "By July 2025, Adobe Analytics found that 38% of consumers had already used generative AI for shopping, with 52% planning to do so within the year. By doing so, the emotional equity brands have built for decades risks becoming underutilized, unless... that emotional connection exists somewhere the algorithm doesn’t control."

At Made, we've been tracking the retail dynamics across continents, working with brands from diverse markets to understand not just existential threats, but opportunities as well.

In the next few years, brands will need to ask themselves some tough questions. What makes the brand truly requestable in a GenAI-driven environment? Should we be optimizing our brand for human emotion or algorithmic relevance? And what happens to the element of brand loyalty when a bot fills the basket?

As Tim Van den Bergh puts it: "AI doesn't eliminate the need for brands, but it redefines what makes them truly valuable. The logos and jingles that once drove sales might soon matter less than the metadata that makes a product “discoverable” in an AI system. Brands need to be aware of this when taking the next step."

Scanning the current market for efforts done by forward-thinking brands brings up a number of strategies that allows brands to navigate this newly shaped e-commerce environment.

Brands can rebalance budgets and invest in structuring product (meta)data including ingredients, benefits, and use cases in ways AI systems can parse and prioritize them.

In that space, we're seeing the French personal care brand L' Oréal partnering with multiple AI platforms to ensure their products surface correctly in voice and chat searches across markets.

Similarly, there's Unilever creating what they call 'digital twins' of products, i.e. rich data profiles that ensure their brands remain discoverable whether a consumer shops in London, Lagos, or Los Angeles.

Brands like Nike and Adidas have shifted focus from competing directly on public shelves to creating unmissable cultural moments.

Zooming in on Nike, they have trained consumers to be hyper-specific in their requests, making their products 'un-substitutable' by AI. By means of many exclusive collaborations (Travis Scott, Off-White), Nike creates products so distinctive that consumers search for them by exact name.

When someone tells their shopping assistant "get me the Travis Scott Jordans," that's a brand winning against algorithmic commoditization.

In China, Perfect Diary built a billion-dollar beauty brand by combining AI-powered personalization with influencer experiences that consumers actively seek out.

These brands understand that when consumers request products by name, they bypass algorithmic gatekeepers entirely.

Procter & Gamble has pioneered direct data partnerships with major retailers globally, co-creating AI shopping experiences that benefit both parties.

In Europe, Nestlé works directly with retailers on "algorithmic category management," ensuring their products meet the specific parameters each retailer's AI prioritizes.

Both aren't fighting the algorithm; they're helping to shape it.

The opportunity extends beyond defense to offense. Brands that master AI optimization while maintaining human connection can actually strengthen their position.

Consider Oatly, which transformed oat milk from commodity to cultural phenomenon by creating such distinctive brand language that consumers specifically request it, whether shopping in Stockholm or San Francisco.

Or look at how Korean beauty brands like Glow Recipe built global followings by combining unique formulations with social storytelling that makes their products AI-proof. Consumers don't want "a moisturizer," they want "that plum moisturizer from TikTok."

"The key is what we call 'algorithmic brand architecture': building brands that excel in both human and machine discovery. This means investing in rich product data and structured metadata frameworks, while simultaneously creating cultural relevance that drives specific requests", Tim notes.

With generative AI redefining e-commerce, brands navigating this transition should be focusing on strategic transformation rather than tactical adjustments.

"At Made, we're looking at an approach that centers on three pillars of AI growth strategy", Tim explains. "First, we're helping brands reimagine their entire product portfolio through the lens of AI discoverability. We conduct deep market research to identify which product attributes will drive algorithmic preference, which innovations will create new categories that AI systems must acknowledge, and how your portfolio architecture should evolve to maximize both human appeal and machine recognition."

"In addition, we work with brands to develop sophisticated metadata frameworks that go far beyond basic product information, creating structured data hierarchies that AI systems can interpret and prioritize, and establishing the technical foundations that ensure products surface when consumers prompt AI assistants with complex, multi-attribute queries."

"And last but not least, we guide brands in crafting narratives that transcend algorithmic commoditization. This strategic work involves development of innovation roadmaps that create sustained differentiation from private labels, and building partnership strategies with retailers that position a brand as essential to their AI shopping experiences rather than replaceable."

Ultimately, the rise of GenAI in the global retail landscape will be a story of strategic evolution, not necessarily one of extinction.

Just as brands adapted to supermarkets, e-commerce, and mobile shopping through fundamental business model innovation, they'll adapt to AI-mediated commerce.

"But only if they start with the right strategic foundation today", Tim explains. "Because the real question isn’t whether consumers will still love brands. Of course they will. It’s more about whether the machines that serve them will love them. And that's where an innovation studio like Made comes into play. So here it is; an invitation to all brands out there to co-explore how your brand can build a competitive advantage in an algorithm-driven marketplace."